Neural Network Keras error: setting array element with sequence
I am uploading dummy data to the neural network, but I get an error that I cannot debug:
Here is my data rendered:
df:
Label Mar
0 | [[.332, .326], [.058, .138]]
0 | [[.234, .246], [.234, .395]]
1 | [[.084, .23], [.745, .923]],
I'm trying to use the Mar column to predict the Label column (I know this data doesn't make sense, it just looks like my real data). Here is my neural network code:
model = Sequential()
model.add(Dense(3, input_dim=(1), activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
X = df['Mar']
Y = pd.get_dummies(df['Label'])
model.fit(X, Y, epochs=150, batch_size=10)
Here is the code to create my data:
Sample = [{'Label': 0, 'Mar': [[.332, .326], [.058, .138]]},
{'Label': 0, 'Mar': [[.234, .246], [.013, .592]]},
{'Label': 1, 'Mar': [[.084, .23], [.745, .923]]}]
df = pd.DataFrame(Sample)
When I get to the last line of this code, I get this error:
Epoch 1/150
-----------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-271-3d2506918d89> in <module>()
----> 1 model.fit(X, Y, epochs=150, batch_size=10)
/usr/local/lib/python2.7/site-packages/keras/models.pyc in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)
854 class_weight=class_weight,
855 sample_weight=sample_weight,
--> 856 initial_epoch=initial_epoch)
857
858 def evaluate(self, x, y, batch_size=32, verbose=1,
/usr/local/lib/python2.7/site-packages/keras/engine/training.pyc in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)
1496 val_f=val_f, val_ins=val_ins, shuffle=shuffle,
1497 callback_metrics=callback_metrics,
-> 1498 initial_epoch=initial_epoch)
1499
1500 def evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None):
/usr/local/lib/python2.7/site-packages/keras/engine/training.pyc in _fit_loop(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)
1150 batch_logs['size'] = len(batch_ids)
1151 callbacks.on_batch_begin(batch_index, batch_logs)
-> 1152 outs = f(ins_batch)
1153 if not isinstance(outs, list):
1154 outs = [outs]
/usr/local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc in __call__(self, inputs)
2227 session = get_session()
2228 updated = session.run(self.outputs + [self.updates_op],
-> 2229 feed_dict=feed_dict)
2230 return updated[:len(self.outputs)]
2231
/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
776 try:
777 result = self._run(None, fetches, feed_dict, options_ptr,
--> 778 run_metadata_ptr)
779 if run_metadata:
780 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/usr/local/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
952 np_val = subfeed_val.to_numpy_array()
953 else:
--> 954 np_val = np.asarray(subfeed_val, dtype=subfeed_dtype)
955
956 if (not is_tensor_handle_feed and
/usr/local/lib/python2.7/site-packages/numpy/core/numeric.pyc in asarray(a, dtype, order)
529
530 """
--> 531 return array(a, dtype, copy=False, order=order)
532
533
ValueError: setting an array element with a sequence.
Now I suspect this is because my input columns are lists and not np arrays? However, I tried to make them into arrays first and I still get the same error. It would really love and appreciate the help!
Edit I tried one hot encoding on the label field as I found somewhere on the internet that might help. It didn't help at this point
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Several questions here,
- Irregular shaped entrance
- The input is a mixture of arrays and lists.
One possible solution would be to use keras.layers.Flatten
to modify your data and pd.Series.tolist()
to unify the data type of the input array:
model = Sequential()
model.add(Flatten(input_shape=(2,2)))
model.add(Dense(3, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
X = df['Mar'].tolist()
Y = df['Label']
model.fit(X, Y, epochs=150, batch_size=10)
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